Automatic extraction of endocranial surfaces from CT images of crania

Takashi Michikawa, Hiromasa Suzuki, Masaki Moriguchi, Naomichi Ogihara, Osamu Kondo and Yasushi Kobayashi

PLOS ONE, 2017, vol. 12, issue 4, 1-14

Abstract: The authors present a method for extracting polygon data of endocranial surfaces from CT images of human crania. Based on the fact that the endocast is the largest empty space in the crania, we automate a procedure for endocast extraction by integrating several image processing techniques. Given CT images of human crania, the proposed method extracts endocranial surfaces by the following three steps. The first step is binarization in order to fill void structures, such as diploic space and cracks in the skull. We use a void detection method based on mathematical morphology. The second step is watershed-based segmentation of the endocranial part from the binary image of the CT image. Here, we introduce an automatic initial seed assignment method for the endocranial region using the distance field of the binary image. The final step is partial polygonization of the CT images using the segmentation results as mask images. The resulting polygons represent only the endocranial part, and the closed manifold surfaces are computed even though the endocast is not isolated in the cranium. Since only the isovalue threshold and the size of void structures are required, the procedure is not dependent on the experience of the user. The present paper also demonstrates that the proposed method can extract polygon data of endocasts from CT images of various crania.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0168516

DOI: 10.1371/journal.pone.0168516

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